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The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification , multiclass classification and regression , the structured SVM allows training of a classifier for general structured output labels .
Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
SVM may refer to: Politics. Alliance of Vojvodina Hungarians (Savez vojvođanskih Mađara), a political party in Serbia; Sanjay Vichar Manch, a political party in India;
In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification.
Turkey Cheese Ball. Even if you're not serving turkey this Thanksgiving doesn't mean you can't get in on the theme. Enter: this adorable cheeseball.We used carrots, pecans, pretzels, and bell ...
Support vector machine; H. Hinge loss; L. Least-squares support vector machine; M. Margin (machine learning) R. Radial basis function kernel; Ranking SVM;
From January 2011 to December 2012, if you bought shares in companies when Anne M. Finucane joined the board, and sold them when she left, you would have a 37.9 percent return on your investment, compared to a 12.1 percent return from the S&P 500.